Výsledky vyhledávání - "MARTYNIUK, TETIANA"

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A Controlled Hahn-Mazurkiewicz Theorem and its Applications

Autor: Banakh, Taras, Martyniuk, Tetiana, Nowak, Magdalena, Strobin, Filip

For a metric Peano continuum $X$, let $S_X$ be a Sierpi\'nski function assigning to each $\varepsilon>0$ the smallest cardinality of a cover of $X$ by connected subsets of diameter $\le \varepsilon$. We prove that for any increasing function $\Omega:

Externí odkaz: http://arxiv.org/abs/2305.17200

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DAD-3DHeads: A Large-scale Dense, Accurate and Diverse Dataset for 3D Head Alignment from a Single Image

Autor: Martyniuk, Tetiana, Kupyn, Orest, Kurlyak, Yana, Krashenyi, Igor, Matas, Jiři, Sharmanska, Viktoriia

We present DAD-3DHeads, a dense and diverse large-scale dataset, and a robust model for 3D Dense Head Alignment in the wild. It contains annotations of over 3.5K landmarks that accurately represent 3D head shape compared to the ground-truth scans. Th

Externí odkaz: http://arxiv.org/abs/2204.03688

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FEAR: Fast, Efficient, Accurate and Robust Visual Tracker

Autor: Borsuk, Vasyl, Vei, Roman, Kupyn, Orest, Martyniuk, Tetiana, Krashenyi, Igor, Matas, Jiři

We present FEAR, a family of fast, efficient, accurate, and robust Siamese visual trackers. We present a novel and efficient way to benefit from dual-template representation for object model adaption, which incorporates temporal information with only

Externí odkaz: http://arxiv.org/abs/2112.07957

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DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better

Autor: Kupyn, Orest, Martyniuk, Tetiana, Wu, Junru, Wang, Zhangyang

We present a new end-to-end generative adversarial network (GAN) for single image motion deblurring, named DeblurGAN-v2, which considerably boosts state-of-the-art deblurring efficiency, quality, and flexibility. DeblurGAN-v2 is based on a relativist

Externí odkaz: http://arxiv.org/abs/1908.03826

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